Researchers develop AI that identifies and counts wildlife with 96.6% accuracy

Researchers at Auburn University, Harvard, Oxford, the University of Minnesota, and the University of Wyoming have developed a machine learning algorithm that can identify, describe, and count wildlife with 96.6 percent accuracy. The paper, which was written in November 2017, was accepted in the Proceedings of the National Academy of Sciences (PNAS) this week. "This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behavior into'big data' sciences," Jeff Clune -- associate professor at the the University of Wyoming, senior research manager at Uber's Artificial Intelligence Labs, and senior author of the paper -- said in a statement. "This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems." The researchers trained the computer vision algorithm on 3.2 million images from Snapshot Serengeti, a citizen science project on Zooniverse.org that recruits volunteers to collect images of elephants, giraffes, gazelles, lions, cheetahs, and other animals in their natural habitats.